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Journal ArticleDOI

Tool release: gathering 802.11n traces with channel state information

22 Jan 2011-Vol. 41, Iss: 1, pp 53-53
TL;DR: The measurement setup comprises the customized versions of Intel's close-source firmware and open-source iwlwifi wireless driver, userspace tools to enable these measurements, access point functionality for controlling both ends of the link, and Matlab scripts for data analysis.
Abstract: We are pleased to announce the release of a tool that records detailed measurements of the wireless channel along with received 802.11 packet traces. It runs on a commodity 802.11n NIC, and records Channel State Information (CSI) based on the 802.11 standard. Unlike Receive Signal Strength Indicator (RSSI) values, which merely capture the total power received at the listener, the CSI contains information about the channel between sender and receiver at the level of individual data subcarriers, for each pair of transmit and receive antennas.Our toolkit uses the Intel WiFi Link 5300 wireless NIC with 3 antennas. It works on up-to-date Linux operating systems: in our testbed we use Ubuntu 10.04 LTS with the 2.6.36 kernel. The measurement setup comprises our customized versions of Intel's close-source firmware and open-source iwlwifi wireless driver, userspace tools to enable these measurements, access point functionality for controlling both ends of the link, and Matlab (or Octave) scripts for data analysis. We are releasing the binary of the modified firmware, and the source code to all the other components.

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Citations
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Journal ArticleDOI
18 Mar 2022
TL;DR: Ten major practical and theoretical problems that hinder real deployment of ISAC applications are identified, and possible solutions to those critical challenges are provided.
Abstract: Next-generation mobile communication network (i.e., 6G) has been envisioned to go beyond classical communication functionality and provide integrated sensing and communication (ISAC) capability to enable more emerging ap-plications, such as smart cities, connected vehicles, AIoT and health care/elder care. Among all the ISAC proposals, the most practical and promising approach is to empower existing wireless network (e.g., WiFi, 4G/5G) with the augmented ability to sense the surrounding human and environment, and evolve wireless communication networks into intelligent communication and sensing network (e.g., 6G). In this paper, based on our experience on CSI-based wireless sensing with WiFi/4G/5G signals, we intend to identify ten major practical and theoretical problems that hinder real deployment of ISAC applications, and provide possible solutions to those critical challenges. Hopefully, this work will inspire further research to evolve existing WiFi/4G/5G networks into next-generation intelligent wireless network (i.e., 6G).

9 citations

Journal ArticleDOI
TL;DR: Wang et al. as discussed by the authors proposed a CSI-based human authentication system (CAUTION) which is able to learn distinctive gait features of different users through CSI data to perform human authentication.
Abstract: Existing channel-state information (CSI)-based human authentication systems in the literature require a large amount of CSI data to train deep neural network (DNN) models and are ineffective for unknown intruder detection. To address this issue, we propose a CSI-based human authentication system (CAUTION) which is able to learn distinctive gait features of different users through CSI data to perform human authentication in this article. By taking advantage of few-shot learning, CAUTION is able to construct an accurate user identification model with a very limited number of CSI training data. By converting the CSI samples into low-dimensional representations on the feature plane, it computes central points for different users as their CSI profiles and introduces an intruder threshold to measure whether the CSI data matches one of the user classes by a margin. The intruder threshold is able to be optimized without any intruders’ data. CAUTION does not require a large number of training data and provides an effective way to train the system for unknown intruder detection. We have tested CAUTION at different places and compared it with state-of-the-art CSI-based authentication systems. The experimental results demonstrate that CAUTION is able to perform accurate human authentication with a limited amount of CSI training data (one-fifth of data needed by compared systems) and outperforms the compared human authentication systems.

9 citations

Proceedings ArticleDOI
Ziqi Wang1, Zhihao Gu1, Junwei Yin1, Zhe Chen1, Yuedong Xu1 
08 Oct 2018
TL;DR: ToiFall is proposed, a prototype for syncope detection in toilet environments that collects Channel State Information (CSI) of commodity Wi-Fi devices and shows an accuracy of over 98% for fall detection with satisfying reliability.
Abstract: Syncope and strokes in toilets can lead to severe injuries, and even pose life threats to patients. However, owing to privacy concerns, vision-based fall detection cannot be applied in such a scenario. In this poster, we propose ToiFall, a prototype for syncope detection in toilet environments. ToiFall collects Channel State Information (CSI) of commodity Wi-Fi devices. Different human movements form various textures on CSI images, and such textures can be used for feature extraction and classification. Experimental results show an accuracy of over 98% for fall detection with satisfying reliability.

9 citations


Cites methods from "Tool release: gathering 802.11n tra..."

  • ...The CSITool [4] developed by Halperin et al....

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  • ...CSITool [4] logs CSI on 30 subcarriers for each receiving antenna....

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  • ...The CSITool [4] developed by Halperin et al. is loaded on all three computers for transmitting packets and collecting CSI....

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Proceedings ArticleDOI
10 May 2021
TL;DR: Secure-Pose as mentioned in this paper extracts effective human pose features from synchronized multi-modal signals and detects and localizes forgery traces under both inter-frame and intra-frame attacks in each frame.
Abstract: The cybersecurity breaches render surveillance systems vulnerable to video forgery attacks, under which authentic live video streams are tampered to conceal illegal human activities under surveillance cameras. Traditional video forensics approaches can detect and localize forgery traces in each video frame using computationally-expensive spatial-temporal analysis, while falling short in real-time verification of live video feeds. The recent work correlates time-series camera and wireless signals to recognize replayed surveillance videos using event-level timing information but it cannot realize fine-grained forgery detection and localization on each frame. To fill this gap, this paper proposes Secure-Pose, a novel cross-modal forgery detection and localization system for live surveillance videos using WiFi signals near the camera spot. We observe that coexisting camera and WiFi signals convey common human semantic information and the presence of forgery attacks on video frames will decouple such information correspondence. Secure-Pose extracts effective human pose features from synchronized multi-modal signals and detects and localizes forgery traces under both inter-frame and intra-frame attacks in each frame. We implement Secure-Pose using a commercial camera and two Intel 5300 NICs and evaluate it in real-world environments. Secure-Pose achieves a high detection accuracy of 95.1% and can effectively localize tampered objects under different forgery attacks.

9 citations

Journal ArticleDOI
TL;DR: A novel transfer deep learning-based DFPWL fingerprinting system that uses the CSI extracted from a single link to estimate the location of the target, neither requiring the target to wear any electronic equipment nor deploying a large number of APs and Monitor Devices.
Abstract: Recently, device-free passive wireless indoor localization (DFPWL) has attracted great interest due to the widespread deployment of Wi-Fi devices and the rapid growth in demand for location-based services (LBS). The DFPWL fingerprinting approach based on channel state information (CSI) has become the mainstream method since its simple deployment and localization accuracy. It determines the location of the target from the new measurement CSI by collecting a training database that measures the CSI and using a machine learning classifier. However, we have found through experiments that even if the indoor environment does not change, the CSI fingerprint will be different from the CSI fingerprint in the database over time, and most of the CSI-based DFPWL fingerprinting method ignores this. To cope with the reduction in localization accuracy caused by the time-varying characteristic of CSI, we propose a novel transfer deep learning-based DFPWL system in this paper. It uses the CSI extracted from a single link to estimate the location of the target, neither requiring the target to wear any electronic equipment nor deploying a large number of APs and Monitor Devices. Unlike the other traditional CSI-based DFPWL fingerprinting approaches using the pre-processed CSI samples as fingerprints, our system utilizes the transfer deep learning (TDL) method to learn new feature representations from the CSI samples as fingerprints, which can simultaneously minimize the intra-class differences, maximize inter-class differences, and minimize the distribution differences between fingerprint database and test samples. Finally, the KNN algorithm is utilized to compare the test samples and the fingerprint database under the new feature representation to obtain the estimation location of the target. Experiment results show that our system can effectively improve localization accuracy compared to the other state-of-art, and can maintain stable localization accuracy for a long time without reacquiring the CSI fingerprint database.

9 citations

References
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Proceedings ArticleDOI
30 Aug 2010
TL;DR: It is shown that, for the first time, wireless packet delivery can be accurately predicted for commodity 802.11 NICs from only the channel measurements that they provide, and the rate prediction is as good as the best rate adaptation algorithms for 802.
Abstract: RSSI is known to be a fickle indicator of whether a wireless link will work, for many reasons. This greatly complicates operation because it requires testing and adaptation to find the best rate, transmit power or other parameter that is tuned to boost performance. We show that, for the first time, wireless packet delivery can be accurately predicted for commodity 802.11 NICs from only the channel measurements that they provide. Our model uses 802.11n Channel State Information measurements as input to an OFDM receiver model we develop by using the concept of effective SNR. It is simple, easy to deploy, broadly useful, and accurate. It makes packet delivery predictions for 802.11a/g SISO rates and 802.11n MIMO rates, plus choices of transmit power and antennas. We report testbed experiments that show narrow transition regions (

697 citations


"Tool release: gathering 802.11n tra..." refers methods in this paper

  • ...It works on up-to-date Linux operating systems: in our testbed we use Ubuntu 10.04 LTS with the 2.6.36 kernel....

    [...]

Journal ArticleDOI
01 Oct 2001
TL;DR: The Internet is going mobile and wireless, perhaps quite soon, with a number of diverse technologies leading the charge, including, 3G cellular networks based on CDMA technology, a wide variety of what is deemed 2.5G cellular technologies (e.g., EDGE, GPRS and HDR), and IEEE 802.11 wireless local area networks (WLANs).
Abstract: At some point in the future, how far out we do not exactly know, wireless access to the Internet will outstrip all other forms of access bringing the freedom of mobility to the way we access the we...

615 citations

Journal ArticleDOI
07 Jan 2010
TL;DR: This tutorial provides a brief introduction to multiple antenna techniques, and describes the two main classes of those techniques, spatial diversity and spatial multiplexing.
Abstract: The use of multiple antennas and MIMO techniques based on them is the key feature of 802.11n equipment that sets it apart from earlier 802.11a/g equipment. It is responsible for superior performance, reliability and range. In this tutorial, we provide a brief introduction to multiple antenna techniques. We describe the two main classes of those techniques, spatial diversity and spatial multiplexing. To ground our discussion, we explain how they work in 802.11n NICs in practice.

89 citations